Potential Future Brisbane Clinic Analysis

MonashIVF Group

Introduction

The purpose of the following analysis was to recommend a future clinic site to MonashIVF QLD, based on the optimum middle point in relation to the current clinics and the hospital locations.

The analysis considered the distance and driving duration from the current clinic locations to the future site and on to the hospital.It was necessary to create tables containing the latitudes and longitudes for the current clinics, hospital and potential new sites.

Potential Locations Latitudes and Longitudes

The potential new sites latitudes and longitudes display their location is within a 1km radius of each other on the rivers north side between Auchenflower and Milton. Providing an initial indication that there could be minimal differences in travel time between future sites and the current clinics and hospital.

FutureSites <- select(df_future, c(2:4))

# PLOT WITH LEAFLET
FutureLocation <- leaflet() %>%  addTiles() %>% 
  setView(153.003,-27.475, 14) %>%
  addAwesomeMarkers(lng =df_future$lon, lat = df_future$lat, 
                    icon = icon.orange,
                    label = FutureSites$rooms) %>%
  addCircles(lng = df_future$lon[1], lat = df_future$lat[1], radius = 1000)

FutureLocation

Figure: Potential locations on map of Brisbane

FutureSitesDistance <- osrmTrip(loc = FutureSites)

FutureSitesDistance <- FutureSitesDistance[[1]]$trip

FutureSitesDistanceSummary <- FutureSitesDistance@data %>%
  mutate(distance = round(distance, 1),
  duration = round(duration, 1))

FutureSitesDistanceSummary <- select(FutureSitesDistanceSummary, c(1:2),4)

FutureSitesDistanceSummary <- FutureSitesDistanceSummary %>% 
  rename(
    Start = start,
    End = end,
    'Distance (km)' = distance)

FutureSitesDistanceSummary <- kbl(FutureSitesDistanceSummary) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "responsive"), 
                full_width = F, position = "center")

FutureSitesDistanceSummary

Start	End	Distance (km)
339 Coronation Drive, QLD 4064	Kings Row Office Park, QLD 4064	0.5
Kings Row Office Park, QLD 4064	33 Park Road, QLD 4064	0.7
33 Park Road, QLD 4064	RiverCity Private Hospital, QLD 4066	1.3
RiverCity Private Hospital, QLD 4066	339 Coronation Drive, QLD 4064	1.1

Table: Distance between potential locations

Travel Time

The below visualisation displays an hour-long drive time polygons from the four potential news sites at 10-minute intervals. This visualisation supports the initial findings of the relatively little difference in the possible new sites locations as the polygons are nearly identical. Indicating that you can travel to the same places from each of these sites in relatively the same time.

#Polygons using osrm API and Isochrone
iso1 <- osrmIsochrone(loc = c(df_future$lon[1], df_future$lat[1]), breaks = seq(from = 0,to = 60, by = 10))
iso2 <- osrmIsochrone(loc = c(df_future$lon[2], df_future$lat[2]), breaks = seq(from = 0,to = 60, by = 10))
iso3 <- osrmIsochrone(loc = c(df_future$lon[3], df_future$lat[3]), breaks = seq(from = 0,to = 60, by = 10))
iso4 <- osrmIsochrone(loc = c(df_future$lon[4], df_future$lat[4]), breaks = seq(from = 0,to = 60, by = 10))

#Adding text for legend
iso1@data$drive_times <- factor(paste(iso1@data$min, "to", iso1@data$max, "min"))

#Adding drive time colour
factpal <- colorFactor("RdYlBu", iso1@data$drive_times)

#Map
leaflet() %>%
  addTiles() %>% 
  addMarkers(lng =df_current$lon, lat = df_current$lat, 
             popup = df_current$rooms, label = df_current$rooms) %>%
  addAwesomeMarkers(lng =df_hospital$lon, lat = df_hospital$lat, 
                    label = df_hospital$hospitals, icon = icon.red) %>%
  addAwesomeMarkers(lng =df_future$lon, lat = df_future$lat, icon = icon.orange,
                    label = df_future$rooms) %>%
  setView(153.003,-27.475, 9) %>%
  addPolygons(fill=TRUE, stroke=TRUE, color = "black",
              fillColor = ~factpal(iso1@data$drive_times),
              weight=0.5, fillOpacity=0.2,
              data = iso1) %>% 
  
  addPolygons(fill=TRUE, stroke=TRUE, color = "black",
              fillColor = ~factpal(iso1@data$drive_times),
              weight=0.5, fillOpacity=0.2,
              data = iso2) %>% 
  
  addPolygons(fill=TRUE, stroke=TRUE, color = "black",
              fillColor = ~factpal(iso1@data$drive_times),
              weight=0.5, fillOpacity=0.2,
              data = iso3) %>% 
  
  addPolygons(fill=TRUE, stroke=TRUE, color = "black",
              fillColor = ~factpal(iso1@data$drive_times),
              weight=0.5, fillOpacity=0.2,
              data = iso4) %>% 
  
  addLegend("bottomright", pal = factpal, values = iso1@data$drive_time,   title = "Drive Time")

Figure: Hour-long drive time polygon at 10 minute intervals from possible new site locations

Drive Time Summary

coordinates11 <- list(c(df_current$lon[1], df_current$lat[1]), c(df_future$lon[1], df_future$lat[1]))
coordinates12 <- list(c(df_current$lon[1], df_current$lat[1]), c(df_future$lon[2], df_future$lat[2]))
coordinates13 <- list(c(df_current$lon[1], df_current$lat[1]), c(df_future$lon[3], df_future$lat[3]))
coordinates14 <- list(c(df_current$lon[1], df_current$lat[1]), c(df_future$lon[4], df_future$lat[4]))

coordinates21 <- list(c(df_current$lon[2], df_current$lat[2]), c(df_future$lon[1], df_future$lat[1]))
coordinates22 <- list(c(df_current$lon[2], df_current$lat[2]), c(df_future$lon[2], df_future$lat[2]))
coordinates23 <- list(c(df_current$lon[2], df_current$lat[2]), c(df_future$lon[3], df_future$lat[3]))
coordinates24 <- list(c(df_current$lon[2], df_current$lat[2]), c(df_future$lon[4], df_future$lat[4]))

coordinates31 <- list(c(df_current$lon[3], df_current$lat[3]), c(df_future$lon[1], df_future$lat[1]))
coordinates32 <- list(c(df_current$lon[3], df_current$lat[3]), c(df_future$lon[2], df_future$lat[2]))
coordinates33 <- list(c(df_current$lon[3], df_current$lat[3]), c(df_future$lon[3], df_future$lat[3]))
coordinates34 <- list(c(df_current$lon[3], df_current$lat[3]), c(df_future$lon[4], df_future$lat[4]))

coordinates41 <- list(c(df_current$lon[4], df_current$lat[4]), c(df_future$lon[1], df_future$lat[1]))
coordinates42 <- list(c(df_current$lon[4], df_current$lat[4]), c(df_future$lon[2], df_future$lat[2]))
coordinates43 <- list(c(df_current$lon[4], df_current$lat[4]), c(df_future$lon[3], df_future$lat[3]))
coordinates44 <- list(c(df_current$lon[4], df_current$lat[4]), c(df_future$lon[4], df_future$lat[4]))

coordinatesH1 <- list(c(df_hospital$lon[1], df_hospital$lat[1]), c(df_future$lon[1], df_future$lat[1]))
coordinatesH2 <- list(c(df_hospital$lon[1], df_hospital$lat[1]), c(df_future$lon[2], df_future$lat[2]))
coordinatesH3 <- list(c(df_hospital$lon[1], df_hospital$lat[1]), c(df_future$lon[3], df_future$lat[3]))
coordinatesH4 <- list(c(df_hospital$lon[1], df_hospital$lat[1]), c(df_future$lon[4], df_future$lat[4])) 

# find route 

route11 <- ors_directions(coordinates11, format="geojson")
route12 <- ors_directions(coordinates12, format="geojson")
route13 <- ors_directions(coordinates13, format="geojson")
route14 <- ors_directions(coordinates14, format="geojson")

route21 <- ors_directions(coordinates21, format="geojson")
route22 <- ors_directions(coordinates22, format="geojson")
route23 <- ors_directions(coordinates23, format="geojson")
route24 <- ors_directions(coordinates24, format="geojson")

route31 <- ors_directions(coordinates31, format="geojson")
route32 <- ors_directions(coordinates32, format="geojson")
route33 <- ors_directions(coordinates33, format="geojson")
route34 <- ors_directions(coordinates34, format="geojson")

route41 <- ors_directions(coordinates41, format="geojson")
route42 <- ors_directions(coordinates42, format="geojson")
route43 <- ors_directions(coordinates43, format="geojson")
route44 <- ors_directions(coordinates44, format="geojson")

routeH1 <- ors_directions(coordinatesH1, format="geojson")
routeH2 <- ors_directions(coordinatesH2, format="geojson")
routeH3 <- ors_directions(coordinatesH3, format="geojson")
routeH4 <- ors_directions(coordinatesH4, format="geojson")


TravelTime <- leaflet() %>% addTiles() %>%
  setView(153.003,-27.475, 10) %>%
  addMarkers(lng =df_current$lon, lat = df_current$lat, 
             popup = df_current$rooms, label = df_current$rooms) %>%
  addAwesomeMarkers(lng =df_hospital$lon, lat = df_hospital$lat, label = df_hospital$hospitals, icon = icon.red) %>%
  addAwesomeMarkers(lng =df_future$lon, lat = df_future$lat, icon = icon.orange, label = df_future$rooms)  %>%
  
  addPolygons(fill=TRUE, stroke=TRUE, color = "black",
              fillColor = ~factpal(iso1@data$drive_times),
              weight=0.5, fillOpacity=0.2,
              data = iso1) %>%
  
  addGeoJSON(route11, fill=FALSE) %>% addGeoJSON(route12, fill=FALSE) %>% 
  addGeoJSON(route13, fill=FALSE) %>% addGeoJSON(route14, fill=FALSE) %>%
  
  addGeoJSON(routeH1, fill=FALSE) %>% addGeoJSON(routeH2, fill=FALSE) %>%  
  addGeoJSON(routeH3, fill=FALSE) %>% addGeoJSON(routeH4, fill=FALSE) %>%  
  
  addGeoJSON(route21, fill=FALSE) %>% addGeoJSON(route22, fill=FALSE) %>%  
  addGeoJSON(route23, fill=FALSE) %>% addGeoJSON(route24, fill=FALSE) %>%  
  
  addGeoJSON(route31, fill=FALSE) %>% addGeoJSON(route32, fill=FALSE) %>%  
  addGeoJSON(route33, fill=FALSE) %>% addGeoJSON(route34, fill=FALSE) %>%  
  
  addGeoJSON(route41, fill=FALSE) %>% addGeoJSON(route42, fill=FALSE) %>%  
  addGeoJSON(route43, fill=FALSE) %>% addGeoJSON(route44, fill=FALSE) %>% 
  
  addLegend("bottomright", pal = factpal, values = iso1@data$drive_time,   title = "Drive Time")

TravelTime

Figure: Travel routes from possible new locations to current sites

    # route length in kilometres and duration in hours
    DD11 <-round(unlist(route11$features[[1]]$properties$summary) / c(1000, 60), 2)
    DD11 <- data.frame(t(DD11))
    DD11 <- DD11  %>% add_column(Future = df_future$rooms[1], Current = df_current$rooms[1])
   
    DD12 <-round(unlist(route12$features[[1]]$properties$summary) / c(1000, 60), 2)
    DD12 <- data.frame(t(DD12))
    DD12 <- DD12  %>% add_column(Future = df_future$rooms[2], Current = df_current$rooms[1])
    
    DD13 <-round(unlist(route13$features[[1]]$properties$summary) / c(1000, 60), 2)
    DD13 <- data.frame(t(DD13))
    DD13 <- DD13  %>% add_column(Future = df_future$rooms[3], Current = df_current$rooms[1])
    
    DD14 <-round(unlist(route14$features[[1]]$properties$summary) / c(1000, 60), 2)
    DD14 <- data.frame(t(DD14))
    DD14 <- DD14  %>% add_column(Future = df_future$rooms[4], Current = df_current$rooms[1])
    
    # route length in kilometres and duration in hours
    DD21 <-round(unlist(route21$features[[1]]$properties$summary) / c(1000, 60), 2)
    DD21 <- data.frame(t(DD21))
    DD21 <- DD21  %>% add_column(Future = df_future$rooms[1], Current = df_current$rooms[2])
    
    DD22 <-round(unlist(route22$features[[1]]$properties$summary) / c(1000, 60), 2)
    DD22 <- data.frame(t(DD22))
    DD22 <- DD22  %>% add_column(Future = df_future$rooms[2], Current = df_current$rooms[2])
    
    DD23 <-round(unlist(route23$features[[1]]$properties$summary) / c(1000, 60), 2)
    DD23 <- data.frame(t(DD23))
    DD23 <- DD23  %>% add_column(Future = df_future$rooms[3], Current = df_current$rooms[2])
    
    DD24 <-round(unlist(route24$features[[1]]$properties$summary) / c(1000, 60), 2)
    DD24 <- data.frame(t(DD24))
    DD24 <- DD24  %>% add_column(Future = df_future$rooms[4], Current = df_current$rooms[2])
    
    # route length in kilometres and duration in hours
    DD31 <-round(unlist(route31$features[[1]]$properties$summary) / c(1000, 60), 2)
    DD31 <- data.frame(t(DD31))
    DD31 <- DD31  %>% add_column(Future = df_future$rooms[1], Current = df_current$rooms[3])
    
    DD32 <-round(unlist(route32$features[[1]]$properties$summary) / c(1000, 60), 2)
    DD32 <- data.frame(t(DD32))
    DD32 <- DD32  %>% add_column(Future = df_future$rooms[2], Current = df_current$rooms[3])
    
    DD33 <-round(unlist(route33$features[[1]]$properties$summary) / c(1000, 60), 2)
    DD33 <- data.frame(t(DD33))
    DD33 <- DD33  %>% add_column(Future = df_future$rooms[3], Current = df_current$rooms[3])
    
    DD34 <-round(unlist(route34$features[[1]]$properties$summary) / c(1000, 60), 2)
    DD34 <- data.frame(t(DD34))
    DD34 <- DD34  %>% add_column(Future = df_future$rooms[4], Current = df_current$rooms[3])
    
    # route length in kilometres and duration in hours
    DD41 <-round(unlist(route41$features[[1]]$properties$summary) / c(1000, 60), 2)
    DD41 <- data.frame(t(DD41))
    DD41 <- DD41  %>% add_column(Future = df_future$rooms[1], Current = df_current$rooms[4])
    
    DD42 <-round(unlist(route42$features[[1]]$properties$summary) / c(1000, 60), 2)
    DD42 <- data.frame(t(DD42))
    DD42 <- DD42  %>% add_column(Future = df_future$rooms[2], Current = df_current$rooms[4])
    
    DD43 <-round(unlist(route43$features[[1]]$properties$summary) / c(1000, 60), 2)
    DD43 <- data.frame(t(DD43))
    DD43 <- DD43  %>% add_column(Future = df_future$rooms[3], Current = df_current$rooms[4])
    
    DD44 <-round(unlist(route44$features[[1]]$properties$summary) / c(1000, 60), 2)
    DD44 <- data.frame(t(DD44))
    DD44 <- DD44  %>% add_column(Future = df_future$rooms[4], Current = df_current$rooms[4])
    
    
    DistanceDurations <- rbind(DD11, DD12, DD13, DD14, DD21, DD22, DD23, DD24,
                               DD31, DD32, DD33, DD34, DD41, DD42, DD43, DD44)
    
    
    DistanceDurations <- DistanceDurations[, c(4, 3, 1, 2)]
    
    DistanceDurations <- DistanceDurations %>% 
      rename(
        'Future Sites' = Future,
        'Current Sites' = Current,
        'Duration (min)' = duration,
        'Distance (km)' = distance)

    DistanceDurations <- kbl(DistanceDurations) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "responsive"), 
                full_width = F, position = "center")

DistanceDurations

Current Sites	Future Sites	Distance (km)	Duration (min)
St Andrew’s Place, QLD 4000	339 Coronation Drive, QLD 4064	3.81	6.80
St Andrew’s Place, QLD 4000	33 Park Road, QLD 4064	2.99	5.60
St Andrew’s Place, QLD 4000	Kings Row Office Park, QLD 4064	3.39	6.30
St Andrew’s Place, QLD 4000	RiverCity Private Hospital, QLD 4066	3.78	6.58
Sunnybank Centre for Women, QLD 4109	339 Coronation Drive, QLD 4064	16.03	18.66
Sunnybank Centre for Women, QLD 4109	33 Park Road, QLD 4064	15.79	18.45
Sunnybank Centre for Women, QLD 4109	Kings Row Office Park, QLD 4064	15.78	18.27
Sunnybank Centre for Women, QLD 4109	RiverCity Private Hospital, QLD 4066	16.75	19.68
Northwest Private Hospital, QLD 4053	339 Coronation Drive, QLD 4064	11.96	16.30
Northwest Private Hospital, QLD 4053	33 Park Road, QLD 4064	11.29	15.09
Northwest Private Hospital, QLD 4053	Kings Row Office Park, QLD 4064	11.68	15.78
Northwest Private Hospital, QLD 4053	RiverCity Private Hospital, QLD 4066	11.32	15.22
North Lakes Specialist Medical Centre, QLD 4509	339 Coronation Drive, QLD 4064	38.51	34.29
North Lakes Specialist Medical Centre, QLD 4509	33 Park Road, QLD 4064	37.84	33.09
North Lakes Specialist Medical Centre, QLD 4509	Kings Row Office Park, QLD 4064	38.24	33.78
North Lakes Specialist Medical Centre, QLD 4509	RiverCity Private Hospital, QLD 4066	38.63	34.06

Table: Distance and duration between possible new locations and current sites

The specific distance and driving duration between the potential and current sites support our initial insight. These distances only vary by a couple of kilometers at the max, equating to only a few minutes different.

To continue the drive time analysis, we incorporated the googles historical traffic information to understand if one of the clinics were less likely to be impacted based on their position on the rivers northern bank. However, this is not the case as each potential site is affected by heavy traffic condition in a similar matter.

To finalise the duration and distance analysis the following graph, “Duration and Distance Impact on Current to Potential Future Sites”, was produced displaying the average duration compared to the distance between the current and potential new sites. The graph is then overlaid with the impact of traffic conditions by increasing or decreasing the size of the points. The only noticeable insight from the graph is that there is a minor increase of 3 to 4 minutes in the duration for the River City when traveling to the current clinics, St Andrews, SunnyBank, Northwest, Place Northlake. Otherwise, this graph reiterates our previous findings that their is minimal difference between the potential new site locations and the duration or distance to the current sites, as the future site generate clusters associated with the current sites.

Duration impacted by Traffic Conditions

Graph

#Following code produces an output which can be loaded in to reduce the use of the google API

#Google API connection
#key = "AIzaSyDL8XvPwNR5o_IxnqtocWfN-0m3qAH0fBs"

#Data Transformation for matrix
#current = data.frame(lon = CurrentLocations$lon, lat = CurrentLocations$lat)
#current = as.matrix(current[1:5,])
#future = data.frame(lon = df_future$lon, lat = df_future$lat)
#future = as.matrix(future[1:4,])                             

#doc = as_xml_document(response_matrix)

#doc = mp_matrix(
#  origins = current,
#  destinations =  future,
#  mode = "driving",
#  arrival_time = NULL,
#  departure_time = as.POSIXct(strptime("2021-03-03 18:00:00", "%Y-%m-%d %H:%M:%S")),
#  avoid = NULL,
#  region = NULL,
#  traffic_model = NULL,
#  key,
#  quiet = FALSE)

#pessimistic = data.frame(round(mp_get_matrix(doc, value = "duration_in_traffic_s")/60, 1))
#optimistictime = data.frame(round(mp_get_matrix(doc, value = "duration_s")/60, 1))
#optimistic = data.frame(round(mp_get_matrix(doc, value = "distance_m"), 1))

#Export
#write.csv(optimistic, "optimistic.csv")
#write.csv(optimistictime, "optimistictime.csv")
#write.csv(pessimistic, "pessimistic.csv")

#qick edit in excel faster
##df.long <- pivot_longer(optimistic, cols=1:4, names_to = "TravelLocation", values_to = "Duration")

GoogleOutput <-read.csv("ClinicTravelTimes.csv")

ggplot(GoogleOutput, aes(Distance, Duration, size = Traffic)) + 
  xlim(0, 35) + ylim(0, 37) + labs(colour = "Future Sites") +
  theme_bw() + theme(axis.line = element_line(colour = "darkgrey"),
                     panel.grid.major = element_blank(),
                     panel.grid.minor = element_blank(),
                     panel.border = element_blank(),
                     axis.ticks = element_blank(),
                     panel.background = element_blank(),
                     axis.title  = element_text(colour = "#676767")) +
  geom_point(aes(color = FutureSites)) + 
  labs(x = "Distance from current to future sites") +
  labs(y = "Duration from current to future sites") +
  annotate(geom = "text", x = 26.7, y = 35.6, label = "North Lakes", color = "#676767") +
  annotate(geom = "text", x = 16.5, y = 15.3, label = "SunnyBank", color = "#676767") +
  annotate(geom = "text", x = 12.0, y = 24.7, label = "Northwest", color = "#676767") +
  annotate(geom = "text", x = 6.0, y = 11.2, label = "St Andrew's Place", color = "#676767") +
  annotate(geom = "text", x = 6.2, y = 1.5, label = "The Wesley Hospital", color = "#676767") +
  geom_rect(aes(fill = "The Wesley Hospital"), xmin = 0, xmax = 3,
            ymin = 2.5, ymax = 5.5, alpha = 0.01) +
  geom_rect(aes(fill = "St Andrew's Place"),xmin = 1.4, xmax = 5.5, 
            ymin = 6, ymax = 10.5, alpha = 0.01) +
  geom_rect(aes(fill = "Northwest"),xmin = 11, xmax = 16, 
            ymin = 18, ymax = 24, alpha = 0.01) +
  geom_rect(aes(fill = "SunnyBank"),xmin = 15, xmax = 19, 
            ymin = 16, ymax = 22, alpha = 0.01) +
  geom_rect(aes(fill = "Northlake"),xmin = 27, xmax = 33,
            ymin = 30, ymax = 35, alpha = 0.01) +
  scale_fill_manual(breaks = c("The Wesley Hospital", "St Andrew's Place", "Northwest",
                               "SunnyBank", "Northlake"), 
                    values = c("yellow","green", "deep sky blue", "orange", "pink"),
                    guide = guide_legend(title = "Current Sites", 
                                         override.aes = list(alpha = 0.1)))

Figure: Distance, duration and impact of traffic between possible new locations and current sites

Table

ImpactOfTraffic <- GoogleOutput %>% 
  rename(
    'Future Sites' = FutureSites,
    'Current Sites' = CurrentSites,
    'Duration (min)' = Duration,
    'Distance (km)' = Distance,
    'In Traffic (min)' = Traffic)

ImpactOfTraffic <- kbl(ImpactOfTraffic) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "responsive"), 
                full_width = F, position = "center")

ImpactOfTraffic

Current Sites	Future Sites	Duration (min)	Distance (km)	In Traffic (min)
St Andrew’s Place, QLD 4000	339 Coronation Dr, QLD 4064	7.9	3.5	10.6
Sunnybank Centre for Women, QLD 4109	339 Coronation Dr, QLD 4064	17.9	16.0	24.2
Northwest Private Hospital, QLD 4053	339 Coronation Dr, QLD 4064	20.8	14.7	24.1
North Lakes Specialist Medical Centre, QLD 4509	339 Coronation Dr, QLD 4064	32.4	29.2	38.2
The Wesley Hospital, QLD 4066	339 Coronation Dr, QLD 4064	3.3	0.9	3.2
St Andrew’s Place, QLD 4000	33 Park Rd, QLD 4064	6.7	3.0	8.4
Sunnybank Centre for Women, QLD 4109	33 Park Rd, QLD 4064	17.2	15.8	23.7
Northwest Private Hospital, QLD 4053	33 Park Rd, QLD 4064	19.5	14.3	22.3
North Lakes Specialist Medical Centre, QLD 4509	33 Park Rd, QLD 4064	31.1	28.8	36.5
The Wesley Hospital, QLD 4066	33 Park Rd, QLD 4064	4.1	1.2	4.3
St Andrew’s Place, QLD 4000	Kings Row Office Park, QLD 4064	6.6	2.8	8.2
Sunnybank Centre for Women, QLD 4109	Kings Row Office Park, QLD 4064	17.5	15.9	23.9
Northwest Private Hospital, QLD 4053	Kings Row Office Park, QLD 4064	19.4	14.1	22.1
North Lakes Specialist Medical Centre, QLD 4509	Kings Row Office Park, QLD 4064	31.0	28.6	37.2
The Wesley Hospital, QLD 4066	Kings Row Office Park, QLD 4064	5.0	1.4	5.1
St Andrew’s Place, QLD 4000	RiverCity Private Hospital, QLD 4066	9.6	4.1	11.3
Sunnybank Centre for Women, QLD 4109	RiverCity Private Hospital, QLD 4066	20.8	16.9	27.2
Northwest Private Hospital, QLD 4053	RiverCity Private Hospital, QLD 4066	23.1	12.5	25.4
North Lakes Specialist Medical Centre, QLD 4509	RiverCity Private Hospital, QLD 4066	34.0	29.9	39.6
The Wesley Hospital, QLD 4066	RiverCity Private Hospital, QLD 4066	4.3	0.8	4.2

Table: Distance, duration and impact of traffic between possible new locations and current sites

Location of Public Transport

The final analysis, including reviewing the public transport options around the potential clinics; however, no specific clinic has an advantage over one of the others as all clinics have easy access to the same variations of transport.

BrisbanePublicTransportStops <-read.csv("BrisbanePublicTransportStops.csv")

leaflet() %>%  addTiles() %>% 
  setView(153.003,-27.475, 14) %>%
  addAwesomeMarkers(lng =df_future$lon, lat = df_future$lat, 
                    icon = icon.orange,
                    label = FutureSites$rooms) %>%
  addCircles(lng = df_future$lon[1], lat = df_future$lat[1], radius = 1000, fillColor = "transparent", color = "red") %>%
  addAwesomeMarkers(lng =df_hospital$lon, lat = df_hospital$lat, label = df_hospital$hospitals, icon = icon.red) %>%
  addAwesomeMarkers(lng =BrisbanePublicTransportStops$stop_lon, lat = BrisbanePublicTransportStops$stop_lat, icon = icon.bus)

Figure: Location of public transport in relation to new sites

After reviewing each of the above piece of analysis it is clear there is little different in the proposed location for the future Monash IVF clinic in QLD based on the duration or drive time to the current clinics and hospital.

To truly understand which location would be the best for the future clinic further analysis should be performed to consider other variables that would impact the choice of the future clinic such as costs, number of doctors, etc. This would allow a more in detail cost weighted analysis to be performed to indicate which new site would be most effective and efficient.

 

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